1. Field of the Invention
The present invention relates generally to spelling correction technology. More particularly, the present invention relates to a system and a method for automatically recognizing and correcting misspelled inputs in an electronic device with lower computing power.
2. Description of the Related Art
A mobile device, which includes a broad range of devices and may also be referred to as a portable device, a handheld device, portable communication terminal, etc., is typically a pocket-sized computing device typically having a display screen with touch input and/or a miniature keyboard (often referred to as a keypad). Many different types of mobile devices are being used today as follows: communication devices, mobile computers, handheld game consoles, media recorders, media players/displayers, and personal navigation devices.
Because of space limitations which are based in part on consumer preference, such mobile devices inherently use a smaller sized input unit with a smaller number of keys than a traditional keyboard. Therefore, a mobile device is not user-friendly in that it is typically rather difficult to input letters, and this difficulty is a frequent cause of spelling errors. Such spelling errors can be a source of embarrassment, and in a business setting, can make one think the person sending a message is either careless and/or lacks a basic grasp of the written language.
In an attempt to solve the above-mentioned problem, some approaches have been proposed in the art in which input is automatically corrected. One approach is a word-based correction model. This word-based correction model is a method of searching a pre-established correction dictionary by means of n-gram or edit-distance technique, selecting thereby candidate words for correction, and substituting new words registered in the correction dictionary by using context statistics. The word-based correction model, however, requires a large-sized dictionary capable of containing a great variety of new-coined words and abbreviated words frequently used in colloquial expressions. Thus, one shortcoming of the word-based correction model is the need of an unfavorably a high-capacity memory. Another drawback to this solution is that by trying to reduce memory requirements by having an inventory of stored words often less than a language such as English, for example, there is a tendency for technical terms and idiomatic phrases to be flagged as errors and erroneously “corrected” to a word that is close in spelling to the flagged word.
Another approach is an alphabet-based correction model. This model is a method of dividing words into alphabets, and then deleting or inserting alphabets or substituting new alphabets, based on alphabet conversion statistics obtained from a large-sized learning data. Despite the advantage that an alphabet-based correction model does not require a word dictionary, this model also needs a great memory space for storing a large-sized learning data and statistical information thereof.
For at least some of the above reasons, these conventional above-mentioned approaches may be unavailable for electronic devices with lower computing power (i.e., a low-capacity memory and a poor processing capacity) such as mobile devices.